function individual_predictions_fillGUI(h)

%individual_predictions_fillGUI - fills GUI created by individual_predictions.m
%Syntax: function individual_predictions_fillGUI(h)
%Description: h is the graphic handle

handles=guihandles(h);
dt=get(h,'UserData');
%set(handles.select_sample,'String',dt.dt.samples);
i=get(handles.select_sample,'Value');
axes(handles.time_resolved)
T=[dt.dt.predict.T];
%plot(T,rand(1,length(T)),'o')
for j=1:length(T)
    predict_T=dt.dt.predict(j);
    plot(T(j),predict_T.py(i),'o','MarkerFaceColor','k','MarkerEdgeColor','k');hold on
    %plot(T(j),mean(predict_T.y(i,1:dt.dt.predict(i).OptN)<=predict_T.T),'o','MarkerFaceColor','k','MarkerEdgeColor','k');hold on
end
hold off
xlabel('Progression') 
ylabel('Commitee Vote')
set(gca,'XMinorTick','on','YMinorTick','on','YLim',[0 1])
grid on
set(handles.time_resolved,'Tag','time_resolved')

% Neighborhood map
h=handles.neighbors;
axes(h)
set(h,'Tag','neighbors','XAxisLocation','top','XTickLabel','')
ylabel('reference neighbor');
% Cluster analysis of samples by the committee members
k=size(dt.predict(1).var,1); % number of committees
n=length(dt.predict); %number of episodes
cube=zeros(k,k,n); %collect correlations here
for i=1:n
    [lala,m]=min(mean(dt.predict(i).dy)); %optimal number of variables 
    
    for j=1:size(dt.predict(i).var,1)
        
    end
end




%disp(':-)')